Learning an optimal classification model intrinsically depends on data quality. Despite many efforts for its characterization, existing methods have often limited quality measures to specific criteria, leading to the lack of comprehensive definitions and rigorous formulations. Indeed, its evaluation is related to the context and often requires external elements, which implies a process that is long and prone to errors. Therefore, there is still a strong need for solutions that enable effective data quality assessment. This paper addresses the resulting scientific challenges and introduces a new metric, specifically designed for numerical classification problems. Unlike existing measures, the proposed solution is based on the correlated evolution between classification performance and data deterioration. Therefore, it offers three main advantages: Being model independent, not requiring the use of external reference data while offering a solution that is easy to adapt for several real-world scenarios. Additionally, we provide a comprehensive interpretation of the quality scores and illustrate the main evaluation levels with use cases. We demonstrate its effectiveness through extensive experiments and comparisons with the state of the art.